30 citations as recorded by crossref.

1. Virtual Joint Field Campaign: a framework of synthetic landscapes to assess multiscale measurement methods of water storage T. Francke et al. https://doi.org/10.5194/gmd-18-819-2025
2. Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management Y. Zheng & F. Han https://doi.org/10.1007/s00477-015-1091-8
3. Assessing Effects of Data Limitations on Salinity Forecasting in Barataria Basin, Louisiana, with a Bayesian Analysis E. Habib et al. https://doi.org/10.2112/06-0723.1
4. Over-parameterised, uncertain ‘mathematical marionettes’ — How can we best use catchment water quality models? An example of an 80-year catchment-scale nutrient balance A. Wade et al. https://doi.org/10.1016/j.scitotenv.2008.04.030
5. Parameter sensitivity analysis and prediction error in field-scale NO3-N modelling A. El-Sadek & R. Vázquez https://doi.org/10.1016/j.agwat.2012.05.010
6. An application of the GLUE methodology for estimating the parameters of the INCA-N model K. Rankinen et al. https://doi.org/10.1016/j.scitotenv.2006.02.034
7. Parametric Uncertainty Analysis of Predictive Models in Louisiana's 2012 Coastal Master Plan E. Habib & D. Reed https://doi.org/10.2112/SI_67_9
8. When should stream water be sampled to be most informative for event-based, multi-criteria model calibration? L. Wang et al. https://doi.org/10.2166/nh.2017.197
9. Uncertainty assessment of a process-based integrated catchment model of phosphorus S. Dean et al. https://doi.org/10.1007/s00477-008-0273-z
10. Two-Phase Monte Carlo Simulation for Partitioning the Effects of Epistemic and Aleatory Uncertainty in TMDL Modeling A. Mishra et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001731
11. Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China Z. Shen et al. https://doi.org/10.5194/hess-16-121-2012
12. Bayesian uncertainty assessment of a semi-distributed integrated catchment model of phosphorus transport J. Starrfelt & Ø. Kaste https://doi.org/10.1039/C3EM00619K
13. Nutrient dynamics, transfer and retention along the aquatic continuum from land to ocean: towards integration of ecological and biogeochemical models A. Bouwman et al. https://doi.org/10.5194/bg-10-1-2013
14. Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium) O. Leta et al. https://doi.org/10.1016/j.envsoft.2015.02.010
15. COMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE C. Dormann et al. https://doi.org/10.1890/07-1772.1
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17. Impact of measurement error and limited data frequency on parameter estimation and uncertainty quantification F. Khorashadi Zadeh et al. https://doi.org/10.1016/j.envsoft.2019.03.022
18. Dynamic response of land use and river nutrient concentration to long-term climatic changes G. Bussi et al. https://doi.org/10.1016/j.scitotenv.2017.03.069
19. Modelling nitrogen in the Yeşilirmak River catchment in Northern Turkey: Impacts of future climate and environmental change and implications for nutrient management M. Hadjikakou et al. https://doi.org/10.1016/j.scitotenv.2011.02.038
20. Modeling nutrient in-stream processes at the watershed scale using Nutrient Spiralling metrics R. Marcé & J. Armengol https://doi.org/10.5194/hess-13-953-2009
21. Multiple-response Bayesian calibration of watershed water quality models with significant input and model structure errors F. Han & Y. Zheng https://doi.org/10.1016/j.advwatres.2015.12.007
22. Characterizing fast herbicide transport in a small agricultural catchment with conceptual models L. Ammann et al. https://doi.org/10.1016/j.jhydrol.2020.124812
23. Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States B. Nolan et al. https://doi.org/10.1002/ps.3875
24. Estimating seepage intensities from groundwater level time series by inverse modelling: A sensitivity analysis on wet meadow scenarios D. Cirkel et al. https://doi.org/10.1016/j.jhydrol.2010.02.009
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26. A fast and effective parameterization of water quality models F. Khorashadi Zadeh et al. https://doi.org/10.1016/j.envsoft.2022.105331
27. Considering spatial distribution and functionality of forests in a modeling framework for river basin management C. Lorz et al. https://doi.org/10.1016/j.foreco.2007.02.032
28. Quantifying the Uncertainty of a Conceptual Herbicide Transport Model With Time‐Dependent, Stochastic Parameters L. Ammann et al. https://doi.org/10.1029/2020WR028311
29. Modelling nitrogen fluxes from the land to the coastal zone in European systems: a perspective from the INCA project A. Wade et al. https://doi.org/10.1016/j.jhydrol.2004.07.041
30. Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK) G. Bussi et al. https://doi.org/10.1016/j.scitotenv.2016.02.109